Demo Data from EDBT 2026 / reviewers in the wild / expert
Seungbeom Choi
dblp:232/1355
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-5343-4553ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
GPUs and heterogeneous computing · 72% Cloud and datacenter computing · 28% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing › multi-GPU computing
multi-GPU server |
0.6 | 1 | 2022 | Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing · USENIX ATC 2022 |
GPUs and heterogeneous computing › GPU sharing
spatio-temporal sharing |
0.6 | 1 | 2022 | Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing · USENIX ATC 2022 |
Operating systems › resource management › process management
CPU scheduling |
0.4 | 1 | 2019 | GVTS: Global Virtual Time Fair Scheduling to Support Strict Fairness on Many Cores · IEEE Trans. Parallel Distributed Syst. 2019 |
Operating systems › resource management › process management › CPU scheduling
hierarchical scheduling |
0.4 | 1 | 2019 | GVTS: Global Virtual Time Fair Scheduling to Support Strict Fairness on Many Cores · IEEE Trans. Parallel Distributed Syst. 2019 |
Cloud and datacenter computing
cluster resource management and scheduling |
0.2 | 1 | 2022 | Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing · USENIX ATC 2022 |
Cloud and datacenter computing
inference serving |
0.2 | 1 | 2022 | Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing · USENIX ATC 2022 |
Cloud and datacenter computing › virtualization › virtual machine management
virtual machine scheduling |
0.1 | 1 | 2019 | GVTS: Global Virtual Time Fair Scheduling to Support Strict Fairness on Many Cores · IEEE Trans. Parallel Distributed Syst. 2019 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing
Seungbeom Choi, Sunho Lee 0003, Yeonjae Kim, Jongse Park, Youngjin Kwon, Jaehyuk Huh 0001 |
USENIX ATC | 1 |
| 2019 | GVTS: Global Virtual Time Fair Scheduling to Support Strict Fairness on Many CoresabstractProportional fairness in CPU scheduling has been widely adopted to fairly distribute CPU shares corresponding to their weights. With the emergence of cloud environments, the proportionally fair scheduling has been extended to groups of threads or nested groups to support virtual machines or containers. Such proportional fairness has been supported by popular schedulers, such as Linux Completely Fair Scheduler (CFS) through virtual time scheduling. However, CFS, with a distributed runqueue per CPU, implements the virtual time scheduling locally. Across different queues, the virtual times of threads are not strictly maintained to avoid potential scalability bottlenecks. The uneven fluctuation of CPU shares caused by the limitations of CFS not only violates the fairness support for CPU assignments, but also significantly increases the tail latencies of latency-sensitive applications. To mitigate the limitations of CFS, this paper proposes a global virtual-time fair scheduler (GVTS), which enforces global virtual time fairness for threads and thread groups, even if they run across many physical cores. The new scheduler employs the hierarchical enforcement of target virtual time to enhance the scalability of schedulers, which is aware of the topology of CPU organization. We implemented GVTS in Linux kernel 4.6.4 with several optimizations to provide global virtual time efficiently. Our experimental results show that GVTS can almost eliminate the fairness violation of CFS for both non-grouped and grouped executions. Furthermore, GVTS can curtail the tail latency when latency-sensitive applications are co-running with batch tasks. Changdae Kim 0001, Seungbeom Choi, Jaehyuk Huh 0001 |
IEEE Trans. Parallel Distributed Syst. | 2 |